A method is provided. The method includes: obtaining first environmental information and second environmental information, where the first environmental information and the second environmental information are acquired by different sensors; determining, based on the first environmental information, information about a first lane of a first traffic object in the first environmental information, and determining; and determining whether the first traffic object and the second traffic object have an association relationship.

In a crack detection device (10), an image acquisition unit (21) acquires image data acquired by taking an image of a road surface from an oblique direction with respect to the road surface, An image classification unit (22) classifies image data acquired into an acceptable range with a resolution higher than a standard value, and an unacceptable range with a resolution equal to or less than the standard value. A data output unit (23) outputs acceptable data being image data of a part classified into the acceptable range as data to detect a crack on the road surface. An image display unit (24) displays data output.

A behavior prediction system predicts human behaviors based on environment-aware information such as camera movement data and geospatial data. The system receives sensor data of a vehicle reflecting a state of the vehicle at a given time and a given location. The system determines a field of concern in images of a video stream and determines one or more portions of images of the video stream that correspond to the field of concern. The system may apply different levels of processing powers to objects in the images based on whether an object is in the field of concern. The system then generates features of objects and identify VRUs from the objects of the video stream. For the identified VRUs, the system inputs a representation of the VRUs and the features into a machine learning model, and outputs from the machine learning model a behavioral risk assessment of the VRUs.

Various embodiments, methods and systems for implementing a distributed computing system machine-learning training service are provided. Initially a machine learning model is accessed. A plurality of synthetic data assets are accessed, where a synthetic data asset is associated with asset-variation parameters that are programmable for machine-learning. The machine learning model is retrained using the plurality of synthetic data assets. The machine-learning training service is further configured for executing real-time calls to generate an on-the-fly-generated synthetic data asset such that the on-the-fly-generated synthetic data asset is rendered in real-time to preclude pre-rendering and storing the on-the-fly-generated synthetic data asset. The machine-learning training service further supports hybrid-based machine learning training, where the machine learning model is trained based on a combination of the plurality of synthetic data assets, a plurality of non-synthetic data assets, and synthetic data asset metadata associated with the plurality of synthetic data assets.

Systems and methods for distributed training of deep learning models are disclosed. An example local device to train deep learning models includes a reference generator to label input data received at the local device to generate training data, a trainer to train a local deep learning model and to transmit the local deep learning model to a server that is to receive a plurality of local deep learning models from a plurality of local devices, the server to determine a set of weights for a global deep learning model, and an updater to update the local deep learning model based on the set of weights received from the server.

An information processing device includes: an acquisition unit that acquires a first image by capturing a subject at a first distance, and extracting, from a registered biological information group that includes biological information of a plurality of registrants, a first biological information group that contains biological information of a first person detected from the first image; and a collation unit that collates biological information of a second person detected from a second image obtained by capturing the subject at a second distance, which is shorter than the distance to the subject in the first image with the biological information included in the first biological information group.

A method for data processing, an electronic device, and a computer-readable storage medium, which relate to the field of computers. The method includes: acquiring a scheduling information for a perception model based on a user application; determining, based on the scheduling information for the perception model, a scheduling set of the perception model, where the scheduling set of the perception model comprises one or more sub-models of a plurality of sub-models of the perception model; and running, based on perception data from a data collection device, the one or more sub-models of the scheduling set of the perception model, so as to output one or more perception results corresponding to the one or more sub-models.

This invention provides a system and method that uses a hybrid model for transportation-based (e.g. maritime) visual event detection of events. In operation, video data is reduced by detecting change and exclusively transmitting images to the deep learning model when changes are detected, or alternatively, based upon a timer that samples at selected intervals. Relatively straightforward deep learning models are used, which operate on sparse individual frames, instead of employing complex deep learning models that operate on multiple frames/videos. This approach reduces the need for specialized models. Independent, rule-based classifiers are used, based on the output of the deep learning model into visual events that, in turn, allows highly specialized events to be constructed. For example, multiple detections can be combined into higher-level single events, and thus, the existence maintenance procedures, cargo activities, and/or inspection rounds can be derived from combining multiple events or multiple detections.

An information processing device that is configured to: receive, from each of two or more vehicles, image information captured by an image capture device installed at a vehicle, and vehicle information including position information on the vehicle; in a case in which a dangerously-driven vehicle has been detected by vehicles, establish a priority level for image processing of image information captured by the vehicles that have detected the dangerously-driven vehicle, in accordance with a predetermined condition; and based on the image information, perform image processing to identify a characteristic of the dangerously-driven vehicle in accordance with the established priority level.

An information processing device that is configured to: receive, from each of two or more vehicles, image information captured by an image capture device installed at a vehicle, and vehicle information including position information on the vehicle; in a case in which a dangerously-driven vehicle has been detected by vehicles, establish a priority level for image processing of image information captured by the vehicles that have detected the dangerously-driven vehicle, in accordance with a predetermined condition; and based on the image information, perform image processing to identify a characteristic of the dangerously-driven vehicle in accordance with the established priority level.